IP Threat Report
14.194.98.249
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Malicious Activity | Behavioral anomaly detected by automated analysis | +0 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Recommendations
Actions taken & recommended
- IP 14.194.98.249 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 14.194.98.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ General Security
Add 14.194.98.249 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Neighbors in 14.194.98.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
Blacklist Status (DNSBL)
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
Threat Analysis
14.194.98.249 has been assigned a threat score of 70/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
📊 Threat Analysis
Threat intelligence analysis has linked 14.194.98.249 to malicious activity originating from Faridabad, India, operating on the network of Tata Tele Services GSM. The address has been under observation since its initial detection. During its 26-day observation window, we recorded 20 hostile requests from this IP — roughly 0.8 per day on average. With 102 flagged addresses, India represents a significant presence in our threat database. At 70/100, this IP warrants immediate defensive action.
Related Threats
Security Intelligence
💡 Machine Learning in Threat Detection
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.
💡 Zero-Day Vulnerability Markets
Zero-day vulnerabilities command premium prices in both legitimate and criminal markets. Government agencies, defensive security firms, and criminal organizations compete for these undisclosed flaws, creating a complex ecosystem around vulnerability discovery and disclosure.